DIY Mini Cat Tracking Robot

DIY Mini Cat Tracking Robot Component Documentation

Overview

The DIY Mini Cat Tracking Robot is a compact, Arduino-based IoT device designed to track and monitor the movement and activities of your feline friends. This robot is equipped with various sensors and communication modules to provide real-time data on your cat's whereabouts, ensuring their safety and well-being.

Technical Specifications

Microcontroller: Arduino Nano
 Communication Module: Wi-Fi (ESP8266)
 Sensors:
	+ GPS (NEO-6M)
	+ Accelerometer (ADXL345)
	+ Temperature and Humidity (DHT11)
 Power Supply: 3.7V Li-ion Battery
 Motor: 2 x DC Gear Motor (L293D Motor Driver)

Code Examples

Example 1: Basic Tracking and Data Logging

In this example, we will demonstrate how to use the DIY Mini Cat Tracking Robot to track your cat's movement and log the data to a server.

```cpp
#include <WiFi.h>
#include <GPSSerial.h>

// Wi-Fi credentials
const char ssid = "your_wifi_ssid";
const char password = "your_wifi_password";

// GPS module
GPSSerial gps;

void setup() {
  // Initialize Wi-Fi
  WiFi.begin(ssid, password);
  while (WiFi.status() != WL_CONNECTED) {
    delay(1000);
    Serial.println("Connecting to Wi-Fi...");
  }
  Serial.println("Connected to Wi-Fi");
  Serial.println("Initializing GPS...");
  gps.begin(9600);
}

void loop() {
  // Read GPS data
  gps.read();
  float latitude = gps.latitude;
  float longitude = gps.longitude;
  float speed = gps.speed;

// Send data to server
  WiFiClient client;
  client.setServer("your_server_ip", 80);
  client.print("GET /log_data?lat=");
  client.print(latitude);
  client.print("&lon=");
  client.print(longitude);
  client.print("&speed=");
  client.print(speed);
  client.println(" HTTP/1.1");
  client.println("Host: your_server_ip");
  client.println("Connection: close");
  client.println();
  client.stop();

delay(1000);
}
```

Example 2: Real-time Tracking with Web Interface

In this example, we will demonstrate how to use the DIY Mini Cat Tracking Robot to track your cat's movement in real-time using a web interface.

```cpp
#include <WiFi.h>
#include <GPSSerial.h>
#include <WiFiWeb.h>

// Wi-Fi credentials
const char ssid = "your_wifi_ssid";
const char password = "your_wifi_password";

// GPS module
GPSSerial gps;

WiFiWeb web;

void setup() {
  // Initialize Wi-Fi
  WiFi.begin(ssid, password);
  while (WiFi.status() != WL_CONNECTED) {
    delay(1000);
    Serial.println("Connecting to Wi-Fi...");
  }
  Serial.println("Connected to Wi-Fi");
  Serial.println("Initializing GPS...");
  gps.begin(9600);

// Initialize web interface
  web.begin();
  web.on("/track", track);
}

void loop() {
  web.handle();
}

void track() {
  // Read GPS data
  gps.read();
  float latitude = gps.latitude;
  float longitude = gps.longitude;

// Send data to web interface
  web.send(200, "text/html", "<h1>Cat Tracker</h1><p>Latitude: " + String(latitude) + "</p><p>Longitude: " + String(longitude) + "</p>");
}
```

Example 3: Integration with Machine Learning Model for Cat Behavior Analysis

In this example, we will demonstrate how to use the DIY Mini Cat Tracking Robot to collect data and integrate it with a machine learning model to analyze your cat's behavior.

```cpp
#include <WiFi.h>
#include <GPSSerial.h>
#include <TensorFlowLite.h>

// Wi-Fi credentials
const char ssid = "your_wifi_ssid";
const char password = "your_wifi_password";

// GPS module
GPSSerial gps;

// TensorFlow Lite model
TensorFlowLite tflite;

void setup() {
  // Initialize Wi-Fi
  WiFi.begin(ssid, password);
  while (WiFi.status() != WL_CONNECTED) {
    delay(1000);
    Serial.println("Connecting to Wi-Fi...");
  }
  Serial.println("Connected to Wi-Fi");
  Serial.println("Initializing GPS...");
  gps.begin(9600);

// Initialize TensorFlow Lite model
  tflite.begin("cat_behavior_model.tflite");
}

void loop() {
  // Read GPS data
  gps.read();
  float latitude = gps.latitude;
  float longitude = gps.longitude;
  float speed = gps.speed;

// Prepare data for machine learning model
  float data[3] = {latitude, longitude, speed};

// Run inference on machine learning model
  int behavior = tflite.run(data);

// Analyze behavior
  if (behavior == 0) {
    Serial.println("Cat is sleeping");
  } else if (behavior == 1) {
    Serial.println("Cat is playing");
  } else {
    Serial.println("Cat is exploring");
  }

delay(1000);
}
```

These code examples demonstrate the versatility of the DIY Mini Cat Tracking Robot component and its potential applications in various contexts.